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Elaborating on the concept of context awareness, this book presents up-to-date research and novel framework designs for context-aware mobile sensing. Generic and Energy-Efficient Context-Aware Mobile Sensing proposes novel context-inferring algorithms and generic framework designs that can help readers enhance existing tradeoffs in mobile sensing, especially between accuracy and power consumption.

The book presents solutions that emphasize must-have system characteristics such as energy efficiency, accuracy, robustness, adaptability, time-invariance, and optimal sensor sensing. Numerous application examples guide readers from fundamental concepts to the implementation of context-aware-related algorithms and frameworks.

Covering theory and practical strategies for context awareness in mobile sensing, the book will help readers develop the modeling and analysis skills required to build futuristic context-aware framework designs for resource-constrained platforms.

Includes best practices for designing and implementing practical context-aware frameworks in ubiquitous/mobile sensing
Proposes a lightweight online classification method to detect user-centric postural actions
Examines mobile device-based battery modeling under the scope of battery nonlinearities with respect to variant loads
Unveils a novel discrete time inhomogeneous hidden semi-Markov model (DT-IHS-MM)-based generic framework to achieve a better realization of HAR-based mobile context awareness
Supplying theory and equation derivations for all the concepts discussed, the book includes design tips for the implementation of smartphone programming as well as pointers on how to make the best use of MATLAB® for the presentation of performance analysis. Coverage includes lightweight, online, and unsupervised pattern recognition methods; adaptive, time-variant, and optimal sensory sampling strategies; and energy-efficient, robust, and inhomogeneous context-aware framework designs.

Researchers will learn the latest modeling and analysis research on mobile sensing. Students will gain access to accessible reference material on mobile sensing theory and practice. Engineers will gain authoritative insights into cutting-edge system designs.




Context Awareness for Mobile Sensing

Introduction

Context Awareness Essentials

Contextual Information

Context Representation

ContextModeling

Context-Aware Middleware

Context Inference

Context-Aware Framework Designs

Context-Aware Applications

Health Care andWell-Being Based

Human Activity Recognition Based

Transportation and Location Based

Social Networking Based

Environmental Based

Challenges and Future Trends

Energy Awareness

Adaptive and Opportunistic Sensory Sampling

Modeling the Smart Device Battery Behavior for Energy Optimizations

Data Calibration and Robustness

Efficient Context Inference Algorithms

Generic Context-Aware Framework Designs

Standard Context-Aware Middleware Solutions

Mobile Cloud Computing

Security, Privacy, and Trust

Context Inference: Posture Detection

Discussions

Proposed Classification Method

Standalone Mode

Assisting Mode

Feature Extraction

Pattern Recognition-Based Classification

Gaussian Mixture Model

k-Nearest Neighbors Search

Linear Discriminant Analysis

Online Processing: Dynamic Training

Statistical Tool-Based Classification

Performance Evaluation


Context-Aware Framework: A Basic Design

Discussions

Proposed Framework

Preliminaries

User State Representation

System Adaptability

Time-Variant User State Transition Matrix

Time-Variant Observation Emission Matrix

Update on System Parameters

Entropy Rate

Scaling Problem

Simulations

Preparations

Applied Process

Power Consumption Model

Accuracy Model

Parameter Setups

Results and Discussions

Validation by a Smartphone Application

Observation Analysis

Construction of Observation Emission Matrix

Applied Process

Performance Evaluation


Energy Efficiency in Physical Hardware

Discussions

Battery Modeling

Modeling of Energy Consumption by Sensors

Preliminaries

Modeling of Sensory Operations

Validation by a Smartphone Application

Sensor Management

Battery Case

Sensor Utilization Case

Performance Analysis

Method I (MI)

Method II (MII)

Method III (MIII)


Context-Aware Framework: A Complex Design

Proposed Framework

Context Inference Module

Inhomogeneous Statistical Machine

Basic Definitions and Inhomogeneity

Underlying Process

User State Representation

Time-Variant User State TransitionMatrix

Adaptive Observation Emission Matrix

Accuracy Notifier and Definition of Actions

Sensor Management Module

Sensor Utilization

Trade-Off Analysis

Intuitive Solutions

Method I (MI)

Method II (MII)

Method III (MIII)

Constrained Markov Decision Process-Based Solution

Partially Observable Markov Decision

Process-Based Solution

Myopic Strategy and Sufficient Statistics

Performance Evaluation

Probabilistic Context Modeling

Construction of Hidden Markov Models

General Model

Parallel HMMs

Factorial HMMs

Coupled/Joint HMMs

Observation Decomposed/Multiple Observation HMMs

Hierarchical HMMs

Dynamic Bayesian Networks

Evaluation

Inference

Learning: Forward-Backward Procedure

Extended Forward-Backward Procedure

Model for Multiple Sensors Use

Appendix

References

Index